What does being a leader entail for an industry data scientist? For some practitioners, leadership means managing people and products; for others, the focus might be on developing workflows and practices that help companies succeed. It can even be both! Regardless of the specific definition of your role, your work…

Data scientists still debate whether serious programming chops are necessary for them to excel at their job. If there’s any consensus, though, it’s that it certainly doesn’t hurt to expand your repertoire. For some practitioners, that might mean investing time into learning more languages. …

Bias is a charged term in the fields of data science and machine learning—not in the least because it means so many things for practitioners. Imbalanced statistical distributions are a form of bias, but so is the representation of racial and gender stereotypes in language models’ training data, or the…

Conor O’Sullivan reflects on communication, writing, and the growing importance of algorithmic fairness. — In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Conor O'Sullivan. Conor is a data scientist with experience in the banking and telecommunications industry…

In a recent post about the benefits of data marts, Vicky Yu highlighted a surprising stat: just a few years ago, data scientists would spend up to 80 percent of their work hours wrangling and cleaning data. Even if the percentage is lower these days (as some surveys suggest), that…

Tessa Xie reflects on the twists and turns of data science careers, the benefits of consulting, and the value of writing for a wide audience. — In the Author Spotlight series, TDS Editors chat with members of our community about their career path in data science, their writing, and their sources of inspiration. Today, we’re thrilled to share our conversation with Tessa Xie. Tessa started her career in finance as a quantitative researcher after graduating from…

The ins and outs of a powerful explainable-AI approach — The power and size of machine learning models have grown to new heights in recent years. With greater complexity comes the need for more accountability and transparency—both for the practitioners who build these models and for those who interpret their results. Within the wide field of explainable AI, one approach…

From chatbots to sentiment analysis, we’re seeing an explosion of real-world use cases for textual data. Some of the buzziest innovations in AI revolve around models trained with ever-increasing quantities of text; on the flip side, we can trace many of the challenges the field is facing to limited, unrepresentative…

With so much uncertainty in the past couple of years — from lockdowns and layoffs to global instability and inflation — the very notion of a career path can feel quaint. How can we plan our next year, to say nothing of the next decade, with so much in constant…